The Perspectives of Taylor, Gantt, and Johnson:
How to Improve Production Scheduling
Volume 16, Number 3
September 2010, pp. 243-254
Jeffrey W. Herrmann
A. James Clark School of Engineering
University of Maryland
College Park, MD 20742 USA
(jwh2@umd.edu)
The challenge of improving production scheduling has inspired many different
approaches. This paper examines the key contributions of three individuals:
Frederick W. Taylor, who defined the key planning functions and created a
planning office; Henry L. Gantt, who provided useful charts to improve
scheduling decision-making, and Selmer M. Johnson, who wrote a highly
influential paper on the mathematical analysis of production scheduling
problems. The paper also describes the perspectives that these three represent
and discusses how these perspectives can help one improve production
scheduling.
Keywords: Production Scheduling; Operations Research History
1. Introduction
In manufacturing facilities, production schedules state when certain controllable
activities (e.g., processing of jobs by resources) should take place. Production
schedules coordinate activities to increase productivity and minimize operating costs.
Using production schedules, managers can identify resource conflicts, control the
release of jobs to the shop, ensure that required raw materials are ordered in time,
determine whether delivery promises can be met, and identify time periods available
for preventive maintenance.
The two key problems in production scheduling are “priorities” and “capacity”
(Wight, 1984). In other words, “What should be done first?” and “Who should do
it?” These questions are answered by “the actual assignment of starting and/or
completion dates to operations or groups of operations to show when these must be
done if the manufacturing order is to be completed on time” (Cox et al., 1992). This
is also known as detailed scheduling, operations scheduling, order scheduling, and
shop scheduling.
Unfortunately, many manufacturers have ineffective production scheduling
systems. They produce goods and ship them to their customers, but they use a broken
collection of independent plans that are frequently ignored, periodic meetings where
unreliable information is shared, expediters who run from one crisis to another, and
ad-hoc decisions made by persons who cannot see the entire system. Production
scheduling systems rely on human decision-makers, and many of them need help
dealing with the swampy complexities of real-world scheduling (discussed in detail
by McKay and Wiers, 2004).
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Production scheduling appeared as a distinct management function in the era of
scientific management. We are unaware of any important innovations in the area of
production scheduling before that time. To improve production scheduling,
organizations have adopted many decision support tools, from Gantt charts to
computer-based scheduling systems (Herrmann, 2005, 2006a). Computer software
can be useful. For example, in the 1980s, IBM developed the Logistics Management
System (Fordyce et al., 1992), an innovative scheduling system for semiconductor
manufacturing facilities that was eventually used at six IBM facilities and by some
customers (Fordyce, 2005). Pinedo (1995) and McKay and Wiers (2006) discuss the
design of scheduling decision support systems, and McKay and Wiers (2004)
provide practical guidelines on selecting and implementing scheduling software.
However, information technology is not necessarily the answer. Based on their
survey of hundreds of manufacturing facilities, LaForge and Craighead (1998)
conclude that computer-based scheduling can help manufacturers improve on-time
delivery, respond quickly to customer orders, and create realistic schedules, but
success requires using finite scheduling techniques and integrating them with other
manufacturing planning systems. Finite scheduling uses actual shop floor conditions,
including capacity constraints and the requirements of orders that have already been
released. However, only 25% of the firms responding to their survey used finite
scheduling for part or all of their operations. Integration is also difficult. Only 48%
of the firms said that the computer-based scheduling system received routine data
automatically from other systems, 30% said that a “good deal” of the data are entered
manually, and 21% said that all data are entered manually.
Starting in the 1950s, academic research on scheduling problems has produced
countless papers on the topic. Pinedo (1995) lists a number of important surveys on
production scheduling. Vieira et al. (2003) present a framework for rescheduling,
and Leung (2004) covers both the fundamentals and the most recent advances in a
wide variety of scheduling research topics. However, there are many difficulties in
applying these results because real-world situations often do not match the
assumptions made by scheduling researchers (Dudek et al., 1992).
Studies of production scheduling in industrial practice have also led to the
development of a business process perspective that considers the knowledge
management and organizational aspects of production scheduling (MacCarthy,
2006). This perspective implies that it is important to design production scheduling
processes that take into account knowledge management mechanisms and their
setting within an organizational structure.
This paper describes the key contributions of three individuals: Frederick W.
Taylor, who defined the key planning functions and created a planning office; Henry
L. Gantt, who provided useful charts to improve scheduling decision-making, and
Selmer M. Johnson, who wrote a highly influential paper on the mathematical
analysis of production scheduling problems. Each one took a different approach to
improve production scheduling. This paper reviews their accomplishments and
discusses the perspectives that they adopted. Each perspective looks at the task of
production scheduling in a distinct way and thus proposes a different approach to
improve it. Taylor changed the organization, Gantt created tools to improve decisionmaking, and Johnson solved optimization problems.
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The purpose of this paper is to help production schedulers, engineers, and
researchers better understand the history of production scheduling, show them that
this history provides useful suggestions that are relevant today, and encourage them
to develop more powerful approaches to improve production scheduling.
The story of Gantt charts is commonly discussed in textbooks on scheduling, for
Gantt charts remain an important type of diagram for representing schedules. The
details of the lives and accomplishments of Taylor and Gantt are extensively
documented elsewhere (appropriate citations are included below). Johnson’s
algorithm is extremely well known. Despite the familiarity of these names, the
contribution of this paper is to place these three luminaries into the story of
production scheduling and to relate them to three corresponding perspectives on
production scheduling. Although these perspectives may seem obvious to those who
have worked in the area for many years, the author is unaware of any previous
discussion of them or their roots in the history of production scheduling. For some,
then, this material will be completely new, and presenting it in a single place, with a
structure and context to understand its importance, should be valuable to many.
2. Taylor and the Planning Office
Generally known for his fundamental contributions to scientific management in the
late 1800s, Frederick W. Taylor’s most important contribution to production
scheduling was his creation of the planning office (described in Taylor, 1911). His
separation of planning from execution justified the use of formal scheduling
methods, which became critical as manufacturing organizations grew in complexity.
It established the view that production scheduling is a distinct decision-making
process in which individuals share information, make plans, and react to unexpected
events.
In keeping with the idea of specialized work, there were many different jobs in
Taylor’s planning office, from route clerk to speed boss to inspector (Parkhurst,
1912; Thompson, 1917). Wilson (2000) lists fifteen different positions. Here we
briefly describe some of the positions that are most closely related to scheduling.
The route clerk created and maintained routings that specified the operations
required completing an order and the components needed. The instruction card clerk
wrote job instructions that specified the best way to perform the operations. The
production clerk created and updated a master production schedule based on firm
orders and capacity. The balance of stores clerk maintained sheets (or cards) with the
current inventory level, the amount on order, and the quantity needed for orders. This
clerk also issued replenishment orders. The order of work clerk issued shop orders
and released material to the shop. Recording clerks kept track of the status of each
order by updating the route charts and also creating summary sheets (called progress
sheets). The relative priority of different orders was determined by the
superintendent of production.
An interesting feature of the planning office was the bulletin board. There was one
in the planning office, and another on the shop floor (Thompson, 1917). The bulletin
board had space for every workstation in the shop. The board showed, for each
workstation, the operation that the workstation was currently performing, the orders
currently waiting for processing there, and future orders that would eventually need
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processing there. Thus, it was an important resource for sharing information about
scheduling decisions to many people. White boards that serve the same purpose can
be found today in many shops.
Many firms implemented versions of Taylor’s production planning office, which
performed routing, dispatching (issuing shop orders) and scheduling, “the timing of
all operations with a view to insuring their completion when required” (Mitchell,
1939). The widespread adoption of Taylor’s approach reflects the importance of the
organizational perspective of scheduling, a system-level view that scheduling is part
of the complex flow of information and decision-making that forms the
manufacturing planning and control system (McKay et al., 1995; Herrmann, 2004;
MacCarthy, 2006). The rise of information technology did not eliminate the planning
functions defined by Taylor; it simply automated them using ever more complex
software that is typically divided into modules that perform the different functions
more quickly and accurately than Taylor’s clerks could (see Vollmann et al., 1997,
for a detailed description of modern manufacturing planning and control systems).
3. Gantt and His Charts
The man most commonly identified with production scheduling is Henry L. Gantt,
who not only worked with Taylor at Midvale Steel Company, Simonds Rolling
Machine Company, and Bethlehem Steel but also served as a consultant to many
other firms and government agencies (Alford, 1934). In Work, Wages, and Profits
(originally published in 1916), Gantt explicitly described scheduling, especially in
the job shop environment. He discussed the need to coordinate activities to avoid
“interferences” but warned that the most elegant schedules created by planning
offices are useless if they are ignored.
To improve managerial decision-making, Gantt created innovative charts for
visualizing planned and actual production. A Gantt chart is “the earliest and best
known type of control chart especially designed to show graphically the relationship
between planned performance and actual performance” (Cox et al., 1992).
Gantt designed his charts so that foremen and other supervisors not in the planning
office could quickly know whether production was on schedule, ahead of schedule,
or behind schedule. His charts were improvements to the forms that Taylor
developed for the planning office. Notably, he created charts for the personal use of
supervisors in a format that they could carry with them at all times (unlike Taylor’s
bulletin board, which was useful only when one was near it). Although they were
part of Taylor’s broader manufacturing planning system, Gantt charts were meant to
help individual managers make better decisions (Wilson, 2003).
It is important to note that Gantt created many different types of charts, based on
the specific needs of managers at American Locomotive Company, Brighton Mills,
Frankford Arsenal, and other manufacturing organizations. He also created charts
during World War I to improve the management of new ship construction and
shipping operations (Porter, 1968).
His charts attempt to make schedules useful. In Organizing for Work (originally
published in 1919), Gantt gave two principles for his charts: one, measure activities
by the amount of time needed to complete them; two, use the space on the chart to
represent the amount of the activity that should have been done in that time.
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Clark (1942) provides an excellent overview of the different types of Gantt charts,
including the machine record chart and the man record chart, both of which record
past performance. Of most interest to those studying production scheduling is the
layout chart, which specifies “when jobs are to be begun, by whom, and how long
they will take.”
Gantt’s influential charts are ubiquitous in production scheduling and project
management. Gantt was a pioneer in developing graphical ways to visualize the past
performance of machines or employees, the current status of a shop, and schedules of
future activities. He used time (not just quantity) as a way to measure tasks. He used
horizontal bars to represent the number of parts produced (in progress charts) and to
record working time (in machine records).
Gantt’s work on charts to help supervisors reflects the view that scheduling is a
decision-making process in which schedulers perform a variety of tasks and use both
formal and informal information to accomplish these. We call this view the decisionmaking perspective. To perform this process well, schedulers must address
uncertainty, manage bottlenecks, and anticipate the problems that people cause
(McKay and Wiers, 2004). Gantt charts record and organize clearly the key data
needed to perform these tasks.
4. Johnson and the Flow Shop Scheduling Problem
According to Bellman and Gross (1954), one of the two researchers suggested that
Selmer M. Johnson, their colleague at the RAND Corporation, investigate a bookbinding problem in which a set of books must be first printed and then bound (see
also Dudek et al., 1992). The objective was to minimize the total time needed to
finish all of the books. Johnson analyzed the properties of an optimal solution and
presented an elegant algorithm that constructs an optimal solution. The published
paper (Johnson, 1954) not only analyzed the two-stage flow shop scheduling
problem (a basic result in the theory of production scheduling) but also considered
problems with three or more stages and identified a special case for the three-stage
problem.
This paper was the first of a great deal of work on other versions of the flow shop
scheduling problem and set a standard for the analysis of production scheduling
problems of all kinds from the very beginning. Jackson (1956) generalized Johnson’s
results for a two-machine job shop scheduling problem. Smith (1956) considered
some single-machine scheduling problems with due dates. Both of these early,
notable works cited Johnson’s paper and used the same type of analysis. Bellman
and Gross (1954) addressed a slightly simplified version with a different approach
while employing Johnson’s results. Conway et al. (1967) describe Johnson’s paper
as an important influence, as it was “perhaps the most frequently cited paper in the
field of scheduling.” In particular they noted the importance of its proof that the
solution algorithm was optimal. Johnson’s paper “set a wave of research in motion”
(Dudek et al., 1992). No other early work is so distinguished.
Johnson’s paper epitomizes the problem-solving perspective, in which scheduling
is an optimization problem that must be solved. A great deal of research effort has
been spent developing methods to generate optimal production schedules, and
countless papers discussing this topic have appeared in scholarly journals. Typically,
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such papers formulate scheduling as a combinatorial optimization problem
independent from the manufacturing planning and control system in place. Schedule
generation methods include most of the literature in the area of scheduling.
Interested readers should see Pinedo and Chao (1999), Pinedo (1995), or similar
introductory texts on production scheduling. Researchers will find references such as
Leung (2004) and Brucker (2004) useful for more detailed information about
problem formulation and solution techniques.
Although there exists a significant gap between scheduling theory and practice (as
discussed by Dudek et al., 1992; Portougal and Robb, 2000; and others), researchers
have used better problem-solving to improve real-world production scheduling in
some settings (see, for instance, Zweben and Fox, 1994; Dawande et al., 2004;
Bixby et al., 2006; Newman et al., 2006). It may be that the results of production
scheduling theory are applicable in some, but not all, production environments
(Portougal and Robb, 2000).
5. Perspectives on Production Scheduling
The historic work of Taylor, Gantt, and Johnson demonstrate the importance of three
distinct perspectives on production scheduling: organizational, decision-making, and
problem-solving. These three perspectives form a hierarchy, with the organizational
perspective at the highest level, the decision-making perspective in the middle, and
the problem-solving perspective at the lowest level. Moreover, this progression
corresponds to the historical development of these perspectives. Taylor changed the
organization, then Gantt developed charts to improve decision-making, and finally
Johnson studied the optimization problem.
Figure 1 illustrates this relationship in a conceptual way. Moving among these
three perspectives corresponds to shifting one’s focus from the production planning
organization to one person to one task. Thus, this hierarchy of perspectives does not
correspond to a temporal or spatial decomposition of the manufacturing system.
Instead, it is related to a task-based decomposition of the production scheduling
activity.
The layered structure of Figure 1 is not meant to correspond to the different time
frames of production planning; instead, the layers are different ways to view
production scheduling. Moving from one layer to the layer below is like zooming in
on a scheduling decision, in some sense. In the organization layer, information
moves from person to person through information-gathering and decision-making
tasks. For example, one person sets a due date, another reviews it and negotiates a
new one, and a third uses it to make a schedule. In the decision-making layer, the
scheduler uses this information to solve current problems and avoid future ones. For
instance, he checks the due date to evaluate the progress of an order, tries to get the
due date changed, uses it to persuade someone to work on the order, and enters it
into a scheduling routine. In the problem-solving layer, scheduling algorithms
perform calculations on the data to generate and evaluate schedules. For instance,
due dates can be used to sequence jobs and to measure tardiness.
Consider, for instance, the example of a manufacturing facility with whom the
author worked recently. The facility (part of a larger manufacturing organization)
had many difficulties with delivering orders to customers on time. Often, managers
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realized at the last minute that scheduled work could not be performed due to
previously unknown problems with parts or the manufacturing processes. At that
point, production planners would have to identify other work that could be done.
Because this work was not on the schedule, obtaining the parts and setting up
equipment delayed the time until it could commence. Despite the backlog, workers
were underutilized.
Acquisition
manager
Branch
manager
Monthly
meeting
Report
w/o priorities
Production
scheduler
Update
weekly schedule
Shop
foreman
Supervise
production
Report
hours worked
Discuss
Weekly
Finish and sign
weekly schedule
Schedule
Report
W/O status
Production
Engineer
Organizational Perspective
Schedule
production
Report
Shop status
Decision-Making Perspective
Situation assessment
Crisis identification
Rote scheduling
Scenario update
Rescheduling
Constraint relaxation
Find future problems
Problem - Solving Perspective
M1
J1
M2
J4
J1
M3
J5
J4
J2
J6
J3
J2
J1
J3
J2
J6
J3
J6
Figure 1 Perspectives on Production Scheduling
The facility managers were considering installing a scheduling software system
that another facility in the organization had developed for its own use. The software
had been developed originally as a workload planning tool to generate quotes and
plan capacity. Over time, it had been expanded so that various managers and
planners in the other facility could generate special reports about customer orders,
material and resource requirements, the shop schedule, trouble tickets, and similar
items.
A study of the production scheduling process (conducted by the author and the
facility’s production planners) showed that different individuals (planners and
supervisors) in the facility maintained a variety of schedules and that workers could
update the schedules directly when they had knowledge of problems that affected
production. Unfortunately, this duplication and decentralization naturally meant a
lack of coordination among the different areas in the facility and between the
planners (who were scheduling the orders) and the shop floor personnel (who knew
that those orders could not be done).
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PERSPECTIVES OF TAYLOR, GANTT, AND JOHNSON
The scheduling problem itself was not very difficult to describe. Although the
facility was essentially a job shop, the routings were not long or complicated.
Priorities were established based on customer importance and due dates. Capacity
was sufficient for the workload. Conflicts were settled and schedules were updated
in meetings with supervisors, planners, and managers, who did not complain about
the complexity of the scheduling task.
From the organizational perspective, it is clear that the facility described above had
problems with the flow of information and decision-making authority. No one had a
complete picture of the facility’s schedule, those creating schedules did not have the
information that they needed to generate good schedules, and others were able to
change the schedule without their approval.
The facility managers viewed the problem from the decision-making perspective
and therefore investigated purchasing software with the hope that new software
would solve the facility’s problems by improving scheduling decision-making.
From the problem-solving perspective, one would view this as a problem of
scheduling a job shop with uncertain job ready times, which would lead one to
develop and test algorithms to generate schedules that are robust to that uncertainty.
These three perspectives suggest that there are multiple ways to improve
production scheduling. Adopting the organizational perspective leads one to
understand and improve the flow of information so that scheduling decision-makers
have access to valuable, up-to-date, and accurate data and can share the results of
their decisions to the right people at the right time (MacCarthy, 2006). Adopting the
decision-making perspective leads one to setup daily scheduling routines for the
schedulers, to provide the schedulers with training on the scheduling software, and to
help schedulers improve their ability to identify future problems. Adopting the
problem-solving perspective leads one to develop better mathematical models and
algorithms that generate more efficient schedules. These approaches complement
each other and can be combined as an integrative strategy that starts by adopting the
organizational perspective and ends by considering the problem-solving perspective
(Herrmann, 2006b).
In general, those who wish to improve production scheduling must call upon a
variety of techniques, identify the appropriate tools, and adapt them creatively for a
particular situation. In addition, they should use multiple perspectives and involve
the persons that have the problem in order to increase understanding of the realworld situation, which is an important objective (cf. Hall, 1985; Meredith, 2001).
6. Conclusions
From a hundred years of work on improving production scheduling, Taylor, Gantt,
and Johnson stand out for their original and influential contributions, which represent
three important and distinct perspectives: the organizational perspective, the
decision-making perspective, and the problem-solving perspective.
As shown in this paper (cf. Figure 1), these perspectives can be positioned relative
to one another in a hierarchical structure. Moreover, this structure is relevant to other
decision-making systems and provides a comprehensive approach to attack the
swampy complexities of real world messes, as the earliest work in operations
research did (Miser, 1987). Because it addresses messy problems, requires the use of
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multiple techniques, and focuses on understanding the situation, improving
production scheduling is a challenging example of management engineering, an
under-developed area of operations research that falls between the straightforward
application of existing techniques and the research activities that add to our body of
knowledge (Corbett and Van Wassenhove, 1993).
The focus in this paper on these three reflects our emphasis on production
scheduling and is not meant to deny the contributions of many other researchers to
related areas. Like Gantt, Frank Gilbreth and others worked with Taylor to create and
describe the field of scientific management (see, for instance, Gilbreth, 1914; and
Hunt, 1924). Nevertheless, Taylor was acknowledged as the founder of the
movement, and Gantt spent more effort on improving scheduling decision-making
than his contemporaries did.
Johnson was part of an active and important research community centered at
UCLA and RAND. This group, whose leaders included Mel Salveson and James
Jackson, created the foundations of operations management (Singhal et al., 2007).
At the same time, Holt, Modigliani, Muth, and Simon were developing, with their
colleagues at the Carnegie Institute of Technology, influential methods for aggregate
production planning and forecasting (Singhal and Singhal, 2007). Other researchers
studied not only scheduling problems like the flow shop problem but also
transportation problems, industrial applications in linear programming, and related
areas (Bellman, 1956).
The use of computers for project scheduling (initially) and production scheduling
was another important development (see, for example, O’Brien, 1969) because
computers can automatically generate large and complex Gantt charts and can
quickly execute the solution algorithms that researchers developed. This technology
did not, however, introduce a new perspective on scheduling.
We have identified and discussed the historical sources of three important
perspectives on the problem. Because no one of them is sufficient alone, this paper
describes the relationships between these perspectives and suggests a more
comprehensive and effective approach that uses ideas from all three perspectives to
improve production scheduling.
Directions for future research include studies of different manufacturing settings to
identify the most important aspects of the three perspectives discussed in this paper.
From the organizational perspective, an important challenge is finding the correct
balance of centralization and decentralization in the presence of modern information
technology, which has revolutionized communication and data sharing. Likewise,
Internet-based communication and sensor networks may provide new opportunities
to improve decision-making by increasing production schedulers’ ability to foresee
future problems and identify ways to avoid them. Case studies of applying the
integrated approach discussed in this paper would be valuable as well. Finally,
identifying additional perspectives, perhaps based on knowledge representation or
cognitive processes, and developing corresponding tools would enhance this
integrated approach.
Acknowledgments: The author is grateful for the support of collaborators who
provided opportunities to help improve production scheduling systems, the
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assistance of students who performed the work, and the encouragement of
colleagues, especially Kenneth Fordyce, who shared their valuable time and ideas.
The author also appreciates the useful comments of two anonymous referees.
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